
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.model_selection import train_test_split

from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score, KFold

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor

from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import mean_squared_error


df = pd.read_csv('employee_promotion.csv')
version_dict = {0: 'df'}
df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 54808 entries, 0 to 54807
Data columns (total 13 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   employee_id           54808 non-null  int64  
 1   department            54808 non-null  object 
 2   region                54808 non-null  object 
 3   education             52399 non-null  object 
 4   gender                54808 non-null  object 
 5   recruitment_channel   54808 non-null  object 
 6   no_of_trainings       54808 non-null  int64  
 7   age                   54808 non-null  int64  
 8   previous_year_rating  50684 non-null  float64
 9   length_of_service     54808 non-null  int64  
 10  awards_won            54808 non-null  int64  
 11  avg_training_score    52248 non-null  float64
 12  is_promoted           54808 non-null  int64  
dtypes: float64(2), int64(6), object(5)
memory usage: 5.4+ MB


unique = df.nunique() # get number of unique values
null = df.isnull().sum() # get number of null values
count = df.count() # get total count
percent_null = (null/count*100).round(2) # calculate percent of null values 

df_uncp = pd.concat([unique, null, count, percent_null], axis=1) # concatenate all variables above
df_uncp.columns = ['unique', 'null', 'count', 'percent_null'] # name columns

df_uncp # df unique, null, count, percent


print('dupes: ', df.duplicated().sum())

dupes:  0


print('total percent null: ', df_uncp['percent_null'].sum())

total percent null:  17.64


# get a snapshot of all columns where education is null
df[df.education.isna() == True]


# iterate through selected columns and print displot of all 4 education values to compare to each other
edu_nan_cols = ['age', 'length_of_service', 'no_of_trainings', 'avg_training_score', 'previous_year_rating', 'is_promoted']
for col in edu_nan_cols:
    sns.displot(df[col][df.education.isna() == True])
    sns.displot(df[col][df.education == 'Below Secondary'])
    sns.displot(df[col][df.education == 'Bachelor\'s']) 
    sns.displot(df[col][df.education == 'Master\'s & above'])

/Users/ivansaucedo/opt/anaconda3/envs/aiml/lib/python3.8/site-packages/seaborn/axisgrid.py:392: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig, axes = plt.subplots(nrow, ncol, **kwargs)


# drop null values in education column & create new revision; df drop na v01
df_dna = df.copy()
version_dict[1] = 'df_dna'
df_dna.dropna(subset=['education'], inplace=True)
df_dna


# replace null values with average in previous_year_rating columns
df_dna.previous_year_rating.fillna(df_dna.previous_year_rating.mean(), inplace=True)


# replace null values with average in avg_training_score columns
df_dna.avg_training_score.fillna(df_dna.avg_training_score.mean(), inplace=True)


# confirm no null values
df_dna.isnull().sum()

employee_id             0
department              0
region                  0
education               0
gender                  0
recruitment_channel     0
no_of_trainings         0
age                     0
previous_year_rating    0
length_of_service       0
awards_won              0
avg_training_score      0
is_promoted             0
dtype: int64


# extract only the number from the region string using regex & create new revision: df transform categorical v02
df_tcat = df_dna.copy()
version_dict[2] = 'df_tcat'
df_tcat.region = df_tcat.region.str.extract('(\d+)')
df_tcat


# transform gender into binary
gender = {'m': 1,'f': 0}
df_tcat.gender = [gender[item] for item in df_tcat.gender]
df_tcat


# get the unique values in the education column
df_tcat.education.unique()

array(["Master's & above", "Bachelor's", 'Below Secondary'], dtype=object)


# convert education into sequential tier/rank order
edu = {'Below Secondary': 1,'Bachelor\'s': 2, 'Master\'s & above':3}
df_tcat.education = [edu[item] for item in df_tcat.education]
df_tcat.head()


# get the unique values in the recruitment_channel column
df_tcat.recruitment_channel.unique()

array(['sourcing', 'other', 'referred'], dtype=object)


# one-hot encoding of recruitment_channel & create new revision: df one hot encoding v03
df_ohe = pd.get_dummies(df_tcat, columns=['recruitment_channel'])
version_dict[3] = 'df_ohe'
df_ohe.head()


# get the unique values in the department column
df_ohe.department.unique()

array(['Sales & Marketing', 'Operations', 'Technology', 'Analytics',
       'R&D', 'Procurement', 'Finance', 'HR', 'Legal'], dtype=object)


# one-hot encoding of department
df_ohe = pd.get_dummies(df_ohe, columns=['department'])
df_ohe.head()


df_ohe.dtypes

employee_id                       int64
region                           object
education                         int64
gender                            int64
no_of_trainings                   int64
age                               int64
previous_year_rating            float64
length_of_service                 int64
awards_won                        int64
avg_training_score              float64
is_promoted                       int64
recruitment_channel_other         uint8
recruitment_channel_referred      uint8
recruitment_channel_sourcing      uint8
department_Analytics              uint8
department_Finance                uint8
department_HR                     uint8
department_Legal                  uint8
department_Operations             uint8
department_Procurement            uint8
department_R&D                    uint8
department_Sales & Marketing      uint8
department_Technology             uint8
dtype: object


# transform region column into int64 type
df_ohe.region = df_ohe.region.astype('int64')
df_ohe.dtypes

employee_id                       int64
region                            int64
education                         int64
gender                            int64
no_of_trainings                   int64
age                               int64
previous_year_rating            float64
length_of_service                 int64
awards_won                        int64
avg_training_score              float64
is_promoted                       int64
recruitment_channel_other         uint8
recruitment_channel_referred      uint8
recruitment_channel_sourcing      uint8
department_Analytics              uint8
department_Finance                uint8
department_HR                     uint8
department_Legal                  uint8
department_Operations             uint8
department_Procurement            uint8
department_R&D                    uint8
department_Sales & Marketing      uint8
department_Technology             uint8
dtype: object


# transform all uint8-type columns with int64
for col in df_ohe.columns:
    if df_ohe[col].dtype == 'uint8':
#         print(col)
        df_ohe[col] = df_ohe[col].astype('int64')


df_ohe.dtypes

employee_id                       int64
region                            int64
education                         int64
gender                            int64
no_of_trainings                   int64
age                               int64
previous_year_rating            float64
length_of_service                 int64
awards_won                        int64
avg_training_score              float64
is_promoted                       int64
recruitment_channel_other         int64
recruitment_channel_referred      int64
recruitment_channel_sourcing      int64
department_Analytics              int64
department_Finance                int64
department_HR                     int64
department_Legal                  int64
department_Operations             int64
department_Procurement            int64
department_R&D                    int64
department_Sales & Marketing      int64
department_Technology             int64
dtype: object


df_describe = pd.concat([df_ohe.describe().T, df_ohe.skew().round(3)], axis=1) # concatenate df_ohe.describe and df_ohe.skew, round skew to 3 decimal places
df_describe['mean'] = df_describe['mean'].round(1) # round mean to 1 decimal place
df_describe['std'] = df_describe['std'].round(2) # round std to 2 decimal places
df_describe.rename(columns = {'50%':'median'}, inplace = True) # rename 50% 
df_describe.rename(columns = {0:'skew'}, inplace = True) # rename skew
df_describe = df_describe.loc[:, ['mean', 'median', 'skew', 'std', 'min', 'max']] # recreate dataframe only with selected values
df_describe.iloc[4:11,:] # print only selected columns


for col in df_ohe.columns[4:11]: # iterate through each column in the list of columns: df_ohe.columns[4:11]
    sns.displot(df_ohe[col], kind="kde") # print displot of of selected columns


# view quantiles from 0 to 100% for selected columns
from numpy import arange

df_quantiles = df_ohe.describe(percentiles=[i for i in arange(.1,1, 0.1)]).T
df_quantiles = df_quantiles.iloc[1:,3:15] 
df_quantiles.iloc[3:10,:]


log_cols = ['no_of_trainings', 'age', 'length_of_service', 'avg_training_score'] # list of columns to take logarithm
for col in log_cols:
    sns.displot(df_ohe[col], kind="kde") # print original displot
    sns.displot(np.log(df_ohe[col]), kind="kde") # print displot after taking logarithm of columns 
#     print('red wine, success! ', col)


# compare skew before and after applying logarithm
skew = [[df_ohe.no_of_trainings.skew(), np.log(df_ohe.no_of_trainings).skew()]]
skew.append([df_ohe.age.skew(), np.log(df_ohe.age).skew()])
skew.append([df_ohe.length_of_service.skew(), np.log(df_ohe.length_of_service).skew()])
skew.append([df_ohe.avg_training_score.skew(), np.log(df_ohe.avg_training_score).skew()])
df_skew = pd.DataFrame(skew, columns=['skew', 'log skew'], index=['no_of_trainings', 'age', 'length_of_service','avg_training_score'])
df_skew


# iterate through log_cols defined above and apply logarithm 
for col in log_cols:
    df_ohe[col] = np.log(df_ohe[col])
#     print('red wine, success! ', col)


# confirm skew was transformed
df_ohe.skew()[log_cols]

no_of_trainings       2.034291
age                   0.499550
length_of_service    -0.302036
avg_training_score    0.165607
dtype: float64


df_ohe.columns

Index(['employee_id', 'region', 'education', 'gender', 'no_of_trainings',
       'age', 'previous_year_rating', 'length_of_service', 'awards_won',
       'avg_training_score', 'is_promoted', 'recruitment_channel_other',
       'recruitment_channel_referred', 'recruitment_channel_sourcing',
       'department_Analytics', 'department_Finance', 'department_HR',
       'department_Legal', 'department_Operations', 'department_Procurement',
       'department_R&D', 'department_Sales & Marketing',
       'department_Technology'],
      dtype='object')


sns.scatterplot(x='no_of_trainings', y='is_promoted', data=df_ohe)
plt.show()


sns.scatterplot(x='avg_training_score', y='is_promoted', data=df_ohe)
plt.show()


# heatmap correlation excluding the one hot encoded columns
sns.heatmap(df_ohe.corr().iloc[1:11,1:11])

<AxesSubplot:>


sns.heatmap(df_ohe.corr())

<AxesSubplot:>


df_ohe.corr().round(3)


# pull only the fields that have a correlation equal to or higher than 0.025, and only look at the is_promoted column
df_ohe.corr()[df_ohe.corr().round(3) >= 0.025].loc['is_promoted']

employee_id                          NaN
region                               NaN
education                       0.025438
gender                               NaN
no_of_trainings                      NaN
age                                  NaN
previous_year_rating            0.153118
length_of_service                    NaN
awards_won                      0.195451
avg_training_score              0.171362
is_promoted                     1.000000
recruitment_channel_other            NaN
recruitment_channel_referred         NaN
recruitment_channel_sourcing         NaN
department_Analytics                 NaN
department_Finance                   NaN
department_HR                        NaN
department_Legal                     NaN
department_Operations                NaN
department_Procurement               NaN
department_R&D                       NaN
department_Sales & Marketing         NaN
department_Technology           0.029687
Name: is_promoted, dtype: float64


# reference previous versions of the dataframe
version_dict

{0: 'df', 1: 'df_dna', 2: 'df_tcat', 3: 'df_ohe'}


# correlataion heatmap of the df version: transform categorical
sns.heatmap(df_tcat.corr())

<AxesSubplot:>


sns.pairplot(df_ohe, diag_kind='kde')

<seaborn.axisgrid.PairGrid at 0x7f84339a1f70>


sns.pairplot(df_tcat, diag_kind='kde')

<seaborn.axisgrid.PairGrid at 0x7f83ea07b0a0>


# pull only the fields that have a correlation equal to or higher than 0, and only look at the is_promoted column
df_pc = pd.DataFrame(df_ohe.corr()['is_promoted'][df_ohe.corr()['is_promoted'] >= 0]) # add to new dataframe
df_pc = df_pc.drop(['employee_id','is_promoted'], axis='index') # remove employee id and is_promoted rows
df_pc # df positive correlation


# create copy and new version: df feature engineering v04
df_feng = df_ohe.copy()
version_dict[4] = 'df_feng'
df_feng


df_feng['r_edu_age'] = df_feng['education'].values / df_feng['age'].values
df_feng['r_score_serv'] = df_feng['avg_training_score'].values / df_feng['length_of_service'].values
df_feng['r_serv_age'] =  df_feng['age'].values / df_feng['length_of_service'].values
df_feng['r_prevyear_score'] = df_feng['previous_year_rating'].values / df_feng['avg_training_score'].values

df_feng

<ipython-input-227-90caf8c8b4bf>:2: RuntimeWarning: divide by zero encountered in true_divide
  df_feng['r_score_serv'] = df_feng['avg_training_score'].values / df_feng['length_of_service'].values
<ipython-input-227-90caf8c8b4bf>:3: RuntimeWarning: divide by zero encountered in true_divide
  df_feng['r_serv_age'] =  df_feng['age'].values / df_feng['length_of_service'].values


df_feng.corr()


# pull only the fields that have a correlation equal to or higher than 0, and only look at the is_promoted column
df_fepc = pd.DataFrame(df_feng.corr()['is_promoted'][df_feng.corr()['is_promoted'] >= 0]) # add to new dataframe
df_fepc = df_fepc.drop(['employee_id','is_promoted'], axis='index') # remove employee id and is_promoted rows
df_fepc # df feature eng positive correlation


df_feng.describe()


df_feng.drop(['r_score_serv', 'r_serv_age'], axis = 1, inplace=True)
df_feng.describe()


# pull only the fields that have a negative correlation equal to or lower than 0, and only look at the is_promoted column
df_nc = pd.DataFrame(df_ohe.corr()['is_promoted'][df_ohe.corr()['is_promoted'] <= 0]) # add to new dataframe
df_nc # df positive correlation


df_feng.drop(['region', 'gender', 'no_of_trainings', 'age', 'length_of_service',
       'recruitment_channel_other', 'recruitment_channel_sourcing',
       'department_Finance', 'department_HR', 'department_Legal',
       'department_R&D', 'department_Sales & Marketing'], axis=1, inplace=True)
df_feng


X = df_feng.drop(['is_promoted'], axis = 1)
y = df_feng['is_promoted']


X_train, X_validation, y_train, y_validation = train_test_split(X, y , test_size=0.3, random_state=88)


k =  KFold(random_state=88, n_splits=5, shuffle=True)


algo= []
cv_r2_mean = []
cv_r2_std = []
cv_rmse_mean = []


pipeline = Pipeline([
    ('scaler',StandardScaler()),
    ('reg', LinearRegression())
])

pipeline.fit(X,y)
algo.append('Linear Regression')

scores = cross_val_score(pipeline, X, y, cv=k)

cv_r2_mean.append(scores.mean())
cv_r2_std.append(scores.std())

cv_rmse_mean.append((-cross_val_score(pipeline, X, y, cv=k,scoring='neg_mean_squared_error').mean())**0.5)


model = DecisionTreeRegressor(random_state=88)

algo.append('Decision Tree')

scores = cross_val_score(model, X, y, cv=k)

cv_r2_mean.append(scores.mean())
cv_r2_std.append(scores.std())

cv_rmse_mean.append((-cross_val_score(model, X, y, cv=k,scoring='neg_mean_squared_error').mean())**0.5)


model = RandomForestRegressor(random_state=88)

#model.fit(xtrain,ytrain)

algo.append('Random Forest')

scores = cross_val_score(model, X, y, cv=k)

cv_r2_mean.append(scores.mean())
cv_r2_std.append(scores.std())

cv_rmse_mean.append((-cross_val_score(model, X, y, cv=k, scoring='neg_mean_squared_error').mean())**0.5)


model = AdaBoostRegressor(random_state=88)

algo.append('AdaBoost')
scores = cross_val_score(model, X, y, cv=k)

cv_r2_mean.append(scores.mean())
cv_r2_std.append(scores.std())

cv_rmse_mean.append((-cross_val_score(model, X, y, cv=k,scoring='neg_mean_squared_error').mean())**0.5)


model = GradientBoostingRegressor(random_state=88)

algo.append('Gradient Boosting')

scores = cross_val_score(model, X, y, cv=k)

cv_r2_mean.append(scores.mean())
cv_r2_std.append(scores.std())

cv_rmse_mean.append((-cross_val_score(model, X, y, cv=k,scoring='neg_mean_squared_error').mean())**0.5)


results = pd.DataFrame()
results['Model'] = algo
results['CV R2 score mean'] = cv_r2_mean
results['CV R2 score std'] = cv_r2_std
results['CV RMSE'] = cv_rmse_mean
results = results.set_index('Model')
results


# removed features that were negatively correlated
# results02 = results.copy()
results02


# initial run
# results01 = results.copy()
results01


rf = RandomForestRegressor(random_state=88, n_jobs=-1)

params = {
    'max_depth': [1,3,5,10,20,40,50,60],
    'min_samples_split': [10,50,100,500],
    'n_estimators': [10,25,100,200,500]
}    

grid = RandomizedSearchCV(rf, params, cv = k, n_jobs=-1, n_iter=30)

grid.fit(X,y)

RandomizedSearchCV(cv=KFold(n_splits=5, random_state=88, shuffle=True),
                   estimator=RandomForestRegressor(n_jobs=-1, random_state=88),
                   n_iter=30, n_jobs=-1,
                   param_distributions={'max_depth': [1, 3, 5, 10, 20, 40, 50,
                                                      60],
                                        'min_samples_split': [10, 50, 100, 500],
                                        'n_estimators': [10, 25, 100, 200,
                                                         500]})


grid.best_params_

{'n_estimators': 200, 'min_samples_split': 10, 'max_depth': 10}


grid_results = pd.DataFrame(grid.cv_results_)
grid_results


plt.plot(grid_results['param_min_samples_split'],
         grid_results['mean_test_score'],
        'bo')

[<matplotlib.lines.Line2D at 0x7f83d0d838b0>]


plt.plot(grid_results['param_n_estimators'],
         grid_results['mean_test_score'],
        'bo')

[<matplotlib.lines.Line2D at 0x7f83cd2f7820>]


plt.plot(grid_results['param_n_estimators'],
         grid_results['mean_test_score'],
        'bo')

[<matplotlib.lines.Line2D at 0x7f83cc90a640>]


rf = RandomForestRegressor(random_state=88, n_jobs=-1)

params = {
    'max_depth': [13,15,17],
    'min_samples_split': [3,5,8],
    'n_estimators': [150,200,250]
}    

grid_search = GridSearchCV(rf, params, cv = k, n_jobs=-1)

grid_search.fit(X,y)

GridSearchCV(cv=KFold(n_splits=5, random_state=88, shuffle=True),
             estimator=RandomForestRegressor(n_jobs=-1, random_state=88),
             n_jobs=-1,
             param_grid={'max_depth': [13, 15, 17],
                         'min_samples_split': [3, 5, 8],
                         'n_estimators': [150, 200, 250]})


rs = RandomizedSearchCV(estimator=GradientBoostingRegressor(random_state=42), param_distributions=params, 
                 return_train_score= True, n_jobs=-1, verbose=2, cv = 10, n_iter=500)
rs.fit(X_train, y_train)

/Users/ivansaucedo/opt/anaconda3/envs/aiml/lib/python3.8/site-packages/sklearn/model_selection/_search.py:285: UserWarning: The total space of parameters 27 is smaller than n_iter=500. Running 27 iterations. For exhaustive searches, use GridSearchCV.
  warnings.warn(

Fitting 10 folds for each of 27 candidates, totalling 270 fits


grid_search.best_score_

0.2544053621210665


grid_search.best_params_

{'max_depth': 13, 'min_samples_split': 8, 'n_estimators': 250}

	mean	median	skew	std	min	max
no_of_trainings	1.3	1.0	3.436	0.61	1.0	10.0
age	35.0	33.0	1.014	7.62	20.0	60.0
previous_year_rating	3.3	3.0	-0.327	1.21	1.0	5.0
length_of_service	5.9	5.0	1.728	4.28	1.0	37.0
awards_won	0.0	0.0	6.339	0.15	0.0	1.0
avg_training_score	64.0	62.0	0.404	13.13	39.0	99.0
is_promoted	0.1	0.0	2.936	0.28	0.0	1.0

	min	10%	20%	30%	40%	50%	60%	70%	80%	90%	max
no_of_trainings	1.0	1.0	1.0	1.0	1.0	1.0	1.000000	1.0	1.0	2.0	10.0
age	20.0	27.0	29.0	30.0	32.0	33.0	35.000000	37.0	40.0	46.0	60.0
previous_year_rating	1.0	1.0	3.0	3.0	3.0	3.0	3.337526	4.0	5.0	5.0	5.0
length_of_service	1.0	2.0	3.0	3.0	4.0	5.0	6.000000	7.0	8.0	11.0	37.0
awards_won	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	1.0
avg_training_score	39.0	48.0	51.0	54.0	58.0	62.0	65.000000	71.0	79.0	83.0	99.0
is_promoted	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	1.0

	employee_id	region	education	gender	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	...	recruitment_channel_sourcing	department_Analytics	department_Finance	department_HR	department_Legal	department_Operations	department_Procurement	department_R&D	department_Sales & Marketing	department_Technology
employee_id	1.000	0.003	0.004	-0.002	-0.004	0.000	0.004	-0.000	0.008	-0.000	...	0.004	0.000	0.007	0.009	-0.006	-0.002	0.005	-0.005	-0.003	-0.003
region	0.003	1.000	-0.076	0.104	-0.002	-0.239	-0.022	-0.141	0.008	0.023	...	0.000	0.105	-0.019	-0.041	-0.019	0.030	-0.070	0.014	0.009	-0.030
education	0.004	-0.076	1.000	-0.017	-0.038	0.419	0.010	0.305	0.000	0.016	...	-0.001	-0.043	-0.053	-0.007	-0.054	-0.001	0.060	0.050	0.005	0.012
gender	-0.002	0.104	-0.017	1.000	0.090	-0.002	-0.022	-0.012	0.003	-0.031	...	0.004	0.146	0.017	-0.054	0.049	-0.123	-0.134	0.073	0.152	-0.075
no_of_trainings	-0.004	-0.002	-0.038	0.090	1.000	-0.088	-0.059	-0.059	-0.008	0.048	...	-0.010	0.062	0.023	-0.077	-0.044	-0.078	0.031	0.037	0.028	0.007
age	0.000	-0.239	0.419	-0.002	-0.088	1.000	0.007	0.640	-0.007	-0.055	...	-0.003	-0.097	-0.092	-0.025	-0.022	0.083	0.047	-0.030	0.032	-0.013
previous_year_rating	0.004	-0.022	0.010	-0.022	-0.059	0.007	1.000	0.001	0.027	0.070	...	-0.005	0.055	0.028	0.024	0.006	0.120	-0.012	0.024	-0.129	-0.053
length_of_service	-0.000	-0.141	0.305	-0.012	-0.059	0.640	0.001	1.000	-0.032	-0.039	...	0.003	-0.060	-0.065	-0.024	-0.061	0.072	0.037	-0.035	0.026	-0.012
awards_won	0.008	0.008	0.000	0.003	-0.008	-0.007	0.027	-0.032	1.000	0.067	...	-0.007	0.002	0.007	-0.006	0.001	0.000	0.002	-0.001	-0.009	0.007
avg_training_score	-0.000	0.023	0.016	-0.031	0.048	-0.055	0.070	-0.039	0.067	1.000	...	-0.008	0.481	-0.040	-0.233	-0.029	-0.092	0.216	0.203	-0.671	0.472
is_promoted	0.001	-0.012	0.025	-0.011	-0.024	-0.016	0.153	-0.008	0.195	0.171	...	-0.000	0.012	-0.003	-0.023	-0.018	0.008	0.015	-0.009	-0.028	0.030
recruitment_channel_other	-0.004	0.014	0.012	-0.007	0.014	0.017	-0.015	0.006	0.006	-0.001	...	-0.957	0.000	0.010	0.007	0.004	-0.003	0.004	-0.000	-0.006	-0.005
recruitment_channel_referred	0.000	-0.049	-0.036	0.009	-0.014	-0.046	0.067	-0.033	0.003	0.028	...	-0.128	-0.011	-0.030	0.032	-0.008	-0.001	-0.028	-0.002	-0.023	0.073
recruitment_channel_sourcing	0.004	0.000	-0.001	0.004	-0.010	-0.003	-0.005	0.003	-0.007	-0.008	...	1.000	0.003	-0.001	-0.017	-0.002	0.003	0.004	0.001	0.013	-0.016
department_Analytics	0.000	0.105	-0.043	0.146	0.062	-0.097	0.055	-0.060	0.002	0.481	...	0.003	1.000	-0.073	-0.071	-0.046	-0.169	-0.128	-0.045	-0.209	-0.128
department_Finance	0.007	-0.019	-0.053	0.017	0.023	-0.092	0.028	-0.065	0.007	-0.040	...	-0.001	-0.073	1.000	-0.049	-0.032	-0.116	-0.088	-0.031	-0.144	-0.088
department_HR	0.009	-0.041	-0.007	-0.054	-0.077	-0.025	0.024	-0.024	-0.006	-0.233	...	-0.017	-0.071	-0.049	1.000	-0.031	-0.113	-0.086	-0.030	-0.140	-0.086
department_Legal	-0.006	-0.019	-0.054	0.049	-0.044	-0.022	0.006	-0.061	0.001	-0.029	...	-0.002	-0.046	-0.032	-0.031	1.000	-0.074	-0.056	-0.020	-0.091	-0.056
department_Operations	-0.002	0.030	-0.001	-0.123	-0.078	0.083	0.120	0.072	0.000	-0.092	...	0.003	-0.169	-0.116	-0.113	-0.074	1.000	-0.205	-0.071	-0.333	-0.204
department_Procurement	0.005	-0.070	0.060	-0.134	0.031	0.047	-0.012	0.037	0.002	0.216	...	0.004	-0.128	-0.088	-0.086	-0.056	-0.205	1.000	-0.054	-0.253	-0.156
department_R&D	-0.005	0.014	0.050	0.073	0.037	-0.030	0.024	-0.035	-0.001	0.203	...	0.001	-0.045	-0.031	-0.030	-0.020	-0.071	-0.054	1.000	-0.088	-0.054
department_Sales & Marketing	-0.003	0.009	0.005	0.152	0.028	0.032	-0.129	0.026	-0.009	-0.671	...	0.013	-0.209	-0.144	-0.140	-0.091	-0.333	-0.253	-0.088	1.000	-0.253
department_Technology	-0.003	-0.030	0.012	-0.075	0.007	-0.013	-0.053	-0.012	0.007	0.472	...	-0.016	-0.128	-0.088	-0.086	-0.056	-0.204	-0.156	-0.054	-0.253	1.000

	employee_id	region	education	gender	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	...	recruitment_channel_sourcing	department_Analytics	department_Finance	department_HR	department_Legal	department_Operations	department_Procurement	department_R&D	department_Sales & Marketing	department_Technology
0	65438	7	3	0	0.000000	3.555348	5.0	2.079442	0	3.891820	...	1	0	0	0	0	0	0	0	1	0
1	65141	22	2	1	0.000000	3.401197	5.0	1.386294	0	4.094345	...	0	0	0	0	0	1	0	0	0	0
2	7513	19	2	1	0.000000	3.526361	3.0	1.945910	0	3.912023	...	1	0	0	0	0	0	0	0	1	0
3	2542	23	2	1	0.693147	3.663562	1.0	2.302585	0	3.912023	...	0	0	0	0	0	0	0	0	1	0
4	48945	26	2	1	0.000000	3.806662	3.0	0.693147	0	4.290459	...	0	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
54802	6915	14	2	1	0.693147	3.433987	1.0	0.693147	0	3.891820	...	0	0	0	0	0	0	0	0	1	0
54803	3030	14	2	1	0.000000	3.871201	3.0	2.833213	0	4.356709	...	1	0	0	0	0	0	0	0	0	1
54804	74592	27	3	0	0.000000	3.610918	2.0	1.791759	0	4.025352	...	0	0	0	0	0	1	0	0	0	0
54805	13918	1	2	1	0.000000	3.295837	5.0	1.098612	0	4.369448	...	0	1	0	0	0	0	0	0	0	0
54807	51526	22	2	1	0.000000	3.295837	1.0	1.609438	0	3.891820	...	0	0	0	1	0	0	0	0	0	0

	employee_id	region	education	gender	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	...	department_Legal	department_Operations	department_Procurement	department_R&D	department_Sales & Marketing	department_Technology	r_edu_age	r_score_serv	r_serv_age	r_prevyear_score
0	65438	7	3	0	0.000000	3.555348	5.0	2.079442	0	3.891820	...	0	0	0	0	1	0	0.843799	1.871570	1.709761	1.284746
1	65141	22	2	1	0.000000	3.401197	5.0	1.386294	0	4.094345	...	0	1	0	0	0	0	0.588028	2.953445	2.453445	1.221197
2	7513	19	2	1	0.000000	3.526361	3.0	1.945910	0	3.912023	...	0	0	0	0	1	0	0.567157	2.010382	1.812191	0.766867
3	2542	23	2	1	0.693147	3.663562	1.0	2.302585	0	3.912023	...	0	0	0	0	1	0	0.545917	1.698970	1.591065	0.255622
4	48945	26	2	1	0.000000	3.806662	3.0	0.693147	0	4.290459	...	0	0	0	0	0	1	0.525395	6.189825	5.491853	0.699226
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
54802	6915	14	2	1	0.693147	3.433987	1.0	0.693147	0	3.891820	...	0	0	0	0	1	0	0.582413	5.614710	4.954196	0.256949
54803	3030	14	2	1	0.000000	3.871201	3.0	2.833213	0	4.356709	...	0	0	0	0	0	1	0.516636	1.537727	1.366364	0.688593
54804	74592	27	3	0	0.000000	3.610918	2.0	1.791759	0	4.025352	...	0	1	0	0	0	0	0.830814	2.246592	2.015292	0.496851
54805	13918	1	2	1	0.000000	3.295837	5.0	1.098612	0	4.369448	...	0	0	0	0	0	0	0.606826	3.977243	3.000000	1.144309
54807	51526	22	2	1	0.000000	3.295837	1.0	1.609438	0	3.891820	...	0	0	0	0	0	0	0.606826	2.418124	2.047819	0.256949

Background & Context¶

Objective¶

Data Dictionary¶

Submission Guidelines¶

Scoring Guide¶

Import Packages and Dataset¶

Insights¶

EDA¶

Insights¶

Insights¶

Address Null Values¶

Insights¶

Transform Categorical Columns¶

Transform DTypes¶

EDA, cont.¶

Pre-Graph Insights¶

Post-Graph Insights¶

Quantile Insights¶

Bi-variate Analysis¶

Insights¶

Insights¶

Insights¶

Insights¶

Feature Engineering¶

Insights¶

Insights¶

Insights¶

Models¶

Linear Regression¶

Decision Tree¶

Random Forest¶

AdaBoost¶

Gradient Boosting¶

Hyper-parameter Tuning¶

	employee_id	department	region	education	gender	recruitment_channel	no_of_trainings	age	previous_year_rating	length_of_service	avg_training_score
0	65438	Sales & Marketing	region_7	Master's & above	f	sourcing	1	35	5.0	8	49.0
1	65141	Operations	region_22	Bachelor's	m	other	1	30	5.0	4	60.0
2	7513	Sales & Marketing	region_19	Bachelor's	m	sourcing	1	34	3.0	7	50.0
3	2542	Sales & Marketing	region_23	Bachelor's	m	other	2	39	1.0	10	50.0
4	48945	Technology	region_26	Bachelor's	m	other	1	45	3.0	2	73.0

	unique	null	count	percent_null
employee_id	54808	0	54808	0.00
department	9	0	54808	0.00
region	34	0	54808	0.00
education	3	2409	52399	4.60
gender	2	0	54808	0.00
recruitment_channel	3	0	54808	0.00
no_of_trainings	10	0	54808	0.00
age	41	0	54808	0.00
previous_year_rating	5	4124	50684	8.14
length_of_service	35	0	54808	0.00
awards_won	2	0	54808	0.00
avg_training_score	59	2560	52248	4.90
is_promoted	2	0	54808	0.00

	employee_id	department	region	education	gender	recruitment_channel	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	is_promoted
10	29934	Technology	region_23	NaN	m	sourcing	1	30	NaN	1	0	77.0	0
21	33332	Operations	region_15	NaN	m	sourcing	1	41	4.0	11	0	57.0	0
32	35465	Sales & Marketing	region_7	NaN	f	sourcing	1	24	1.0	2	0	48.0	0
43	17423	Sales & Marketing	region_2	NaN	m	other	3	24	2.0	2	0	48.0	0
82	66013	Sales & Marketing	region_2	NaN	m	sourcing	2	25	3.0	2	0	53.0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
54692	14821	Sales & Marketing	region_2	NaN	f	sourcing	1	35	3.0	7	0	53.0	0
54717	7684	Analytics	region_2	NaN	m	sourcing	1	32	3.0	4	0	86.0	0
54729	1797	HR	region_2	NaN	f	other	1	28	3.0	2	0	53.0	0
54742	38935	Sales & Marketing	region_31	NaN	m	other	1	28	4.0	3	0	47.0	0
54806	13614	Sales & Marketing	region_9	NaN	m	sourcing	1	29	1.0	2	0	NaN	0

	skew	log skew
no_of_trainings	3.435561	2.034291
age	1.013941	0.499550
length_of_service	1.727902	-0.302036
avg_training_score	0.403527	0.165607

	is_promoted
education	0.025438
previous_year_rating	0.153118
awards_won	0.195451
avg_training_score	0.171362
recruitment_channel_referred	0.018459
department_Analytics	0.011733
department_Operations	0.008470
department_Procurement	0.014683
department_Technology	0.029687

	employee_id	region	education	gender	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	...	department_Legal	department_Operations	department_Procurement	department_R&D	department_Sales & Marketing	department_Technology	r_edu_age	r_score_serv	r_serv_age	r_prevyear_score
employee_id	1.000000	0.003448	0.003816	-0.001940	-0.003732	0.000031	0.004309	-0.000043	0.007586	-0.000008	...	-0.006457	-0.002145	0.004760	-0.005463	-0.003372	-0.003194	0.004199	-0.004865	-0.004904	0.004269
region	0.003448	1.000000	-0.076109	0.104107	-0.002367	-0.238613	-0.022493	-0.140516	0.007803	0.023410	...	-0.018935	0.030050	-0.070046	0.013517	0.009061	-0.029905	-0.010340	0.104200	0.080951	-0.026181
education	0.003816	-0.076109	1.000000	-0.017288	-0.037741	0.418837	0.009864	0.305105	0.000495	0.015572	...	-0.054021	-0.000788	0.059840	0.049640	0.004625	0.011519	0.964094	-0.221883	-0.185208	0.007769
gender	-0.001940	0.104107	-0.017288	1.000000	0.089994	-0.001691	-0.022008	-0.011807	0.002721	-0.031485	...	0.049347	-0.122858	-0.133642	0.073239	0.152343	-0.074502	-0.017530	0.007864	0.011339	-0.018438
no_of_trainings	-0.003732	-0.002367	-0.037741	0.089994	1.000000	-0.087983	-0.059099	-0.059028	-0.007656	0.048231	...	-0.043668	-0.077603	0.031413	0.037365	0.027937	0.006601	-0.015791	0.054771	0.041618	-0.069209
age	0.000031	-0.238613	0.418837	-0.001691	-0.087983	1.000000	0.006646	0.640160	-0.007233	-0.054701	...	-0.022477	0.083489	0.047488	-0.029758	0.031521	-0.012510	0.168239	-0.484376	-0.386218	0.013585
previous_year_rating	0.004309	-0.022493	0.009864	-0.022008	-0.059099	0.006646	1.000000	0.000637	0.026890	0.069899	...	0.006490	0.120322	-0.011593	0.024000	-0.128653	-0.052704	0.008026	0.006005	-0.000967	0.990047
length_of_service	-0.000043	-0.140516	0.305105	-0.011807	-0.059028	0.640160	0.000637	1.000000	-0.032284	-0.039330	...	-0.061011	0.071555	0.036736	-0.035148	0.025760	-0.011799	0.149802	-0.902118	-0.897274	0.005349
awards_won	0.007586	0.007803	0.000495	0.002721	-0.007656	-0.007233	0.026890	-0.032284	1.000000	0.066593	...	0.000931	0.000410	0.002337	-0.001408	-0.008562	0.007038	0.002671	0.035513	0.030870	0.016408
avg_training_score	-0.000008	0.023410	0.015572	-0.031485	0.048231	-0.054701	0.069899	-0.039330	0.066593	1.000000	...	-0.029085	-0.092418	0.216256	0.203394	-0.670833	0.471735	0.031238	0.143253	0.031816	-0.062430
is_promoted	0.000751	-0.011738	0.025438	-0.010575	-0.024425	-0.015876	0.153118	-0.007650	0.195451	0.171362	...	-0.017928	0.008470	0.014683	-0.008668	-0.028105	0.029687	0.031281	0.026850	0.007472	0.126655
recruitment_channel_other	-0.004135	0.014206	0.011503	-0.006517	0.013877	0.016845	-0.015076	0.006486	0.005795	-0.000559	...	0.004409	-0.002758	0.004286	-0.000150	-0.006179	-0.005281	0.008141	-0.006113	-0.004125	-0.014924
recruitment_channel_referred	0.000398	-0.048851	-0.036450	0.008714	-0.013583	-0.046139	0.066943	-0.033423	0.003249	0.028287	...	-0.007917	-0.000865	-0.028384	-0.001959	-0.022907	0.072946	-0.027357	0.030724	0.022632	0.063163
recruitment_channel_sourcing	0.004041	0.000097	-0.000836	0.003989	-0.009958	-0.003356	-0.004549	0.003318	-0.006785	-0.007767	...	-0.002104	0.003029	0.004047	0.000727	0.012960	-0.016169	-0.000132	-0.002732	-0.002393	-0.003589
department_Analytics	0.000370	0.105134	-0.042752	0.145812	0.062130	-0.096844	0.054960	-0.059592	0.001643	0.481072	...	-0.046181	-0.168872	-0.128440	-0.044702	-0.208585	-0.128156	-0.016386	0.103602	0.041427	-0.010201
department_Finance	0.007306	-0.018551	-0.052677	0.017249	0.023293	-0.091539	0.027774	-0.064771	0.006594	-0.040056	...	-0.031773	-0.116188	-0.088370	-0.030756	-0.143511	-0.088175	-0.031811	0.045420	0.041657	0.032570
department_HR	0.008834	-0.040508	-0.007284	-0.053947	-0.077457	-0.025289	0.023790	-0.023601	-0.006255	-0.232670	...	-0.031005	-0.113379	-0.086233	-0.030013	-0.140041	-0.086042	-0.004049	-0.010752	0.011946	0.057347
department_Legal	-0.006457	-0.018935	-0.054021	0.049347	-0.043668	-0.022477	0.006490	-0.061011	0.000931	-0.029085	...	1.000000	-0.073685	-0.056043	-0.019505	-0.091013	-0.055919	-0.054127	0.039538	0.043993	0.010117
department_Operations	-0.002145	0.030050	-0.000788	-0.122858	-0.077603	0.083489	0.120322	0.071555	0.000410	-0.092418	...	-0.073685	1.000000	-0.204935	-0.071326	-0.332813	-0.204483	-0.025612	-0.067687	-0.050324	0.131483
department_Procurement	0.004760	-0.070046	0.059840	-0.133642	0.031413	0.047488	-0.011593	0.036736	0.002337	0.216256	...	-0.056043	-0.204935	1.000000	-0.054249	-0.253129	-0.155524	0.048991	-0.006250	-0.023860	-0.040850
department_R&D	-0.005463	0.013517	0.049640	0.073239	0.037365	-0.029758	0.024000	-0.035148	-0.001408	0.203394	...	-0.019505	-0.071326	-0.054249	1.000000	-0.088099	-0.054129	0.065224	0.053793	0.029798	-0.003595
department_Sales & Marketing	-0.003372	0.009061	0.004625	0.152343	0.027937	0.031521	-0.128653	0.025760	-0.008562	-0.670833	...	-0.091013	-0.332813	-0.253129	-0.088099	1.000000	-0.252570	0.000604	-0.092217	-0.018796	-0.041785
department_Technology	-0.003194	-0.029905	0.011519	-0.074502	0.006601	-0.012510	-0.052704	-0.011799	0.007038	0.471735	...	-0.055919	-0.204483	-0.155524	-0.054129	-0.252570	1.000000	0.013623	0.064872	0.013199	-0.110392
r_edu_age	0.004199	-0.010340	0.964094	-0.017530	-0.015791	0.168239	0.008026	0.149802	0.002671	0.031238	...	-0.054127	-0.025612	0.048991	0.065224	0.000604	0.013623	1.000000	-0.106421	-0.093887	0.003926
r_score_serv	-0.004865	0.104200	-0.221883	0.007864	0.054771	-0.484376	0.006005	-0.902118	0.035513	0.143253	...	0.039538	-0.067687	-0.006250	0.053793	-0.092217	0.064872	-0.106421	1.000000	0.984930	-0.012307
r_serv_age	-0.004904	0.080951	-0.185208	0.011339	0.041618	-0.386218	-0.000967	-0.897274	0.030870	0.031816	...	0.043993	-0.050324	-0.023860	0.029798	-0.018796	0.013199	-0.093887	0.984930	1.000000	-0.005094
r_prevyear_score	0.004269	-0.026181	0.007769	-0.018438	-0.069209	0.013585	0.990047	0.005349	0.016408	-0.062430	...	0.010117	0.131483	-0.040850	-0.003595	-0.041785	-0.110392	0.003926	-0.012307	-0.005094	1.000000

	employee_id	region	education	gender	no_of_trainings	age	previous_year_rating	length_of_service	awards_won	avg_training_score	...	department_Legal	department_Operations	department_Procurement	department_R&D	department_Sales & Marketing	department_Technology	r_edu_age	r_score_serv	r_serv_age	r_prevyear_score
count	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	...	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	52399.000000	5.239900e+04	5.239900e+04	52399.000000
mean	39184.187141	14.239184	2.269471	0.696158	0.156515	3.532928	3.337526	1.533522	0.023168	4.137473	...	0.019752	0.212256	0.134850	0.018531	0.291322	0.134335	0.641174	inf	inf	0.807561
std	22598.386766	10.057990	0.477060	0.459920	0.337045	0.205642	1.212211	0.731684	0.150439	0.202777	...	0.139149	0.408909	0.341566	0.134862	0.454376	0.341015	0.122845	NaN	NaN	0.295311
min	1.000000	1.000000	1.000000	0.000000	0.000000	2.995732	1.000000	0.000000	0.000000	3.663562	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.291207	1.072082e+00	1.133879e+00	0.218104
25%	19651.500000	4.000000	2.000000	0.000000	0.000000	3.367296	3.000000	1.098612	0.000000	3.951244	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.567157	2.020559e+00	1.761801e+00	0.684615
50%	39207.000000	13.000000	2.000000	1.000000	0.000000	3.496508	3.000000	1.609438	0.000000	4.127134	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.593948	2.583620e+00	2.172502e+00	0.766867
75%	58738.500000	22.000000	3.000000	1.000000	0.000000	3.663562	4.000000	2.079442	0.000000	4.330733	...	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.770848	3.799691e+00	3.182658e+00	1.017339
max	78298.000000	34.000000	3.000000	1.000000	2.302585	4.094345	5.000000	3.610918	1.000000	4.595120	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.001425	inf	inf	1.355425

	CV R2 score mean	CV R2 score std	CV RMSE
Model
Linear Regression	0.114667	0.007018	0.264857
Decision Tree	-0.529219	0.042597	0.348007
Random Forest	0.174600	0.010003	0.255729
AdaBoost	-0.007928	0.179463	0.282366
Gradient Boosting	0.242597	0.012367	0.244979

	CV R2 score mean	CV R2 score std	CV RMSE
Model
Linear Regression	0.174710	0.005590	0.255717
Decision Tree	-0.499530	0.022689	0.344646
Random Forest	0.267541	0.005571	0.240901
AdaBoost	0.065075	0.152098	0.271905
Gradient Boosting	0.284001	0.008403	0.238192

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_n_estimators	param_min_samples_split	param_max_depth	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
0	16.939270	0.117264	0.049058	0.011314	100	10	50	{'n_estimators': 100, 'min_samples_split': 10,...	0.206666	0.227671	0.229960	0.207001	0.230785	0.220417	0.011138	11
1	0.954085	0.061131	0.013471	0.000135	25	100	3	{'n_estimators': 25, 'min_samples_split': 100,...	0.132872	0.148209	0.149613	0.123968	0.146979	0.140328	0.010157	26
2	2.086960	0.086527	0.011339	0.000211	10	50	40	{'n_estimators': 10, 'min_samples_split': 50, ...	0.221087	0.235295	0.237677	0.213034	0.242897	0.229998	0.011141	9
3	1.668671	0.101171	0.033927	0.002568	100	50	1	{'n_estimators': 100, 'min_samples_split': 50,...	0.081351	0.100641	0.089318	0.086125	0.096505	0.090788	0.006967	28
4	20.840210	0.098424	0.176224	0.042031	500	10	3	{'n_estimators': 500, 'min_samples_split': 10,...	0.134133	0.148188	0.150021	0.123739	0.148873	0.140991	0.010394	24
5	5.614264	0.258157	0.017990	0.000417	25	10	50	{'n_estimators': 25, 'min_samples_split': 10, ...	0.194557	0.214273	0.212465	0.197246	0.221394	0.207987	0.010344	16
6	0.588905	0.031772	0.011295	0.000225	10	500	3	{'n_estimators': 10, 'min_samples_split': 500,...	0.127310	0.141193	0.144098	0.114836	0.136729	0.132833	0.010643	27
7	1.493262	0.136291	0.011476	0.000206	10	50	10	{'n_estimators': 10, 'min_samples_split': 50, ...	0.237565	0.243925	0.245422	0.221708	0.255508	0.240825	0.011157	7
8	24.715356	0.233873	0.071653	0.013266	200	10	10	{'n_estimators': 200, 'min_samples_split': 10,...	0.241452	0.252884	0.262153	0.231601	0.255017	0.248621	0.010800	1
9	29.601928	0.279766	0.064092	0.004789	200	500	20	{'n_estimators': 200, 'min_samples_split': 500...	0.204383	0.221838	0.225512	0.193555	0.217222	0.212502	0.011862	13
10	2.655569	0.182463	0.013370	0.000127	10	10	60	{'n_estimators': 10, 'min_samples_split': 10, ...	0.174150	0.192302	0.186541	0.171530	0.198901	0.184685	0.010465	18
11	58.395502	0.149775	0.214357	0.067472	500	50	10	{'n_estimators': 500, 'min_samples_split': 50,...	0.238503	0.252152	0.259882	0.226424	0.253920	0.246176	0.012104	2
12	2.000267	0.060082	0.015830	0.000985	25	100	5	{'n_estimators': 25, 'min_samples_split': 100,...	0.169243	0.179415	0.184192	0.159569	0.184351	0.175354	0.009613	21
13	12.631443	0.590534	0.038132	0.005575	100	50	10	{'n_estimators': 100, 'min_samples_split': 50,...	0.238923	0.251090	0.260135	0.226219	0.253201	0.245914	0.011991	3
14	2.897875	0.183829	0.013214	0.000245	10	10	40	{'n_estimators': 10, 'min_samples_split': 10, ...	0.174279	0.193035	0.186469	0.171530	0.199105	0.184884	0.010602	17
15	2.378587	0.147371	0.012310	0.000266	10	10	20	{'n_estimators': 10, 'min_samples_split': 10, ...	0.204683	0.218882	0.215051	0.197222	0.221471	0.211462	0.009131	14
16	83.183156	0.804597	0.450697	0.051243	500	100	40	{'n_estimators': 500, 'min_samples_split': 100...	0.236440	0.253744	0.256688	0.224471	0.255964	0.245462	0.012860	5
17	3.977380	0.071712	0.033419	0.001096	100	50	3	{'n_estimators': 100, 'min_samples_split': 50,...	0.133922	0.149079	0.149785	0.124027	0.148655	0.141094	0.010384	23
18	0.824375	0.061372	0.011073	0.000109	10	10	5	{'n_estimators': 10, 'min_samples_split': 10, ...	0.169309	0.181635	0.188459	0.167077	0.182601	0.177816	0.008227	19
19	18.360088	0.358191	0.039622	0.002230	100	10	60	{'n_estimators': 100, 'min_samples_split': 10,...	0.206666	0.227671	0.229918	0.207001	0.230785	0.220408	0.011131	12
20	1.766845	0.057489	0.014608	0.000184	25	50	5	{'n_estimators': 25, 'min_samples_split': 50, ...	0.170366	0.181747	0.185898	0.162967	0.186832	0.177562	0.009357	20
21	1.750723	0.144214	0.015068	0.000695	25	500	5	{'n_estimators': 25, 'min_samples_split': 500,...	0.157840	0.166581	0.170864	0.147433	0.170058	0.162555	0.008860	22
22	92.557526	0.810422	0.502848	0.038606	500	50	40	{'n_estimators': 500, 'min_samples_split': 50,...	0.233881	0.253940	0.256240	0.228421	0.256221	0.245741	0.012066	4
23	19.579130	0.295292	0.044843	0.005311	100	50	40	{'n_estimators': 100, 'min_samples_split': 50,...	0.232746	0.253298	0.255630	0.226714	0.255456	0.244769	0.012453	6
24	5.874663	0.425275	0.018307	0.000791	25	10	40	{'n_estimators': 25, 'min_samples_split': 10, ...	0.194596	0.214739	0.212304	0.197157	0.221732	0.208106	0.010485	15
25	86.690805	5.440012	0.324959	0.103415	500	10	60	{'n_estimators': 500, 'min_samples_split': 10,...	0.210064	0.230636	0.232775	0.209786	0.232026	0.223058	0.010745	10
26	4.038190	0.117740	0.059100	0.001030	200	50	1	{'n_estimators': 200, 'min_samples_split': 50,...	0.081342	0.100357	0.089587	0.086200	0.096424	0.090782	0.006856	29
27	3.560839	0.192950	0.060284	0.003197	200	10	1	{'n_estimators': 200, 'min_samples_split': 10,...	0.081342	0.100357	0.089587	0.086200	0.096424	0.090782	0.006856	29
28	20.349553	0.470240	0.162986	0.029722	500	50	3	{'n_estimators': 500, 'min_samples_split': 50,...	0.134193	0.148151	0.150006	0.123669	0.148803	0.140964	0.010390	25
29	1.687916	0.088348	0.011206	0.000315	10	100	10	{'n_estimators': 10, 'min_samples_split': 100,...	0.236442	0.241659	0.238712	0.217263	0.250300	0.236875	0.010875	8